ObjectivesIt appears that not only depression, but also low life satisfaction (LS), is related to sleep disorder in the general population. We evaluate whether the prevalence of sleep disorder attributable to depressed mood is greater among participants with low LS.Setting, participants and outcome measuresAnalysis of cross-sectional data from 3880 cohort members from the German Heinz Nixdorf Recall study (2006–2008) aged 51–81 years. Standard mood (Center for Epidemiological Studies Depression scale (CES-D) for Depressive symptoms and a single-item life satisfaction measure) and sleep quality (Pittsburgh Sleep Quality Index, PSQI) measures were conducted as part of the survey. Multiple imputation was used to deal with missing data in outcome, exposures or covariates. Relative excess risk for interaction (RERI) and its 95% CIs were estimated using adjusted prevalence ORs. Owing to the study size, the precision of the measures of additive interaction is relatively low.ResultsWe observed an association between depressed mood (5-units increase in CES-D score) (POR=1.7 (95% CI 1.6 to 1.8)) and sleep disorder, and between low LS (not very satisfied vs very satisfied) (POR=1.5 (1.1 to 2.2)) and sleep disorder. Also, we observed a synergistic effect between lower level of LS (not very satisfied) and depressed mood (score ≥16) on prevalence of sleep disorders (RERI=3.7 (−0.2 to 7.1)). Furthermore, these findings were corroborated in sensitivity analysis carried out with the complete case data set and in sex-specific analyses (RERI=5.5 (−0.4 to 11.3), and RERI=2.4 (−2.5 to 7.4) for men and women, respectively).ConclusionsBoth depressed mood and LS are notably associated with sleep quality, and these relationships are best captured by considering their joint effects. Depression and LS need to be taken into consideration when analysing sleep quality.
BackgroundThe use of multiple licit and illicit substances plays an important role in many university students’ lives. Previous research on multiple substance use patterns of university students, however, often fails to examine use of different illicit substances and/or hookah. Our objective was to complement and advance the current knowledge about common consumption patterns regarding illicit substances and hookah use in this group.MethodsStudents from eight German universities completed an online survey as part of the INSIST study (‘INternet-based Social norms Intervention for the prevention of substance use among STudents’) regarding their consumption of alcohol, tobacco, hookah, cannabis and other illicit substances. Cluster analysis identified distinct consumption patterns of concurrent and non-concurrent substance use and multinomial logistic regressions described key sociodemographic factors associated with these clusters.ResultsSix homogeneous groups were identified: ‘Alcohol Abstainers’ (10.8%), ‘Drinkers Only’ (48.2%), ‘Drinkers and Cigarette Smokers’ (14.6%), ‘Cannabis and Licit Substance Users’ (11.2%), ‘Hookah Users with Co-Use’ (9.8%) and ‘Illicit Substance Users with Co-Use’ (5.4%). Illicit substance use clustered with the consumption of alcohol, tobacco and cannabis. Hookah use was regularly associated with alcohol consumption, less commonly associated with tobacco or cannabis use and very rarely associated with use of other illicit substances. Individuals consuming licit and illicit substances or hookah were mostly male and lived together with other students. Characteristics such as the number of years an individual had spent studying at a university, subject of study, immigrant background and religious affiliation were less commonly associated with cluster membership.ConclusionsAlthough we found substance use patterns in our sample largely similar to previous reports, we identified an important subgroup of individuals using both illicit and licit substances. These individuals may benefit especially from targeted interventions that focus on modifying addictive behavior patterns.Trial registration
DRKS00007635. Registered 17 December 2014 (retrospectively registered).
We recently reported an increased risk of uveal melanoma among mobile phone users. Here, we present the results of a case–control study that assessed the association between mobile phone use and risk of uveal melanoma. We recruited 459 uveal melanoma case patients at the University of Duisburg-Essen and matched 455 case patients with 827 population control subjects, 133 with 180 ophthalmologist control subjects, and 187 with 187 sibling control subjects. We used a questionnaire to assess mobile phone use and estimated odds ratios (ORs) and 95% confidence intervals (95% CIs) of risk for uveal melanoma using conditional logistic regression. Risk of uveal melanoma was not associated with regular mobile phone use (OR = 0.7, 95% CI = 0.5 to 1.0 vs population control subjects; OR = 1.1, 95% CI = 0.6 to 2.3 vs ophthalmologist control subjects; and OR = 1.2, 95% CI = 0.5 to 2.6 vs sibling control subjects), and we observed no trend for cumulative measures of exposure. We did not corroborate our previous results that showed an increased risk of uveal melanoma among regular mobile phone users.
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